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An Introduction to Multiscale Modeling. Scientific Computing and Numerical Analysis Seminar CAAM 699. Outline. Multiscale Nature of Matter Physical Scales Temporal Scales Different Laws for Different Scales Computational Difficulties Homogeneous Elastic String

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an introduction to multiscale modeling

An Introduction to Multiscale Modeling

Scientific Computing

and Numerical Analysis Seminar

CAAM 699

outline
Outline
  • Multiscale Nature of Matter
    • Physical Scales
    • Temporal Scales
  • Different Laws for Different Scales
  • Computational Difficulties
  • Homogeneous Elastic String
  • Inhomogeneous Elastic String
  • Overview of Seminar Topics
physical scales
Physical Scales
  • Discrete Nature of Matter
    • Multiple Physical (Spatial) scales Exist
    • Example: River
    • Physical Scale: km = 103 m

http://ak.water.usgs.gov/yukon/index.php

physical scales4
Physical Scales
  • Water Drops
  • Physical Scale:
    • mm = 10-3 m
  • Water Cluster
  • Physical Scale:
    • 5 nm = 5 x 10-9 m

http://www.btinternet.com/~martin.chaplin/clusters.html

http://eyeofthefish.org/leaky-leushke/

physical scales5
Physical Scales
  • Water Molecule
  • Physical Scale:

0.278 nm = 2.78 x 10-10 m

http://commons.wikimedia.org/wiki/File:Water_molecule.png

temporal scales
Temporal Scales
  • Multiple Time Scales in Matter
  • Time Scale of Interest Depends on Phenomenon of Interest
  • Fluid Time Scales:
    • River Flow: hours
    • Rain Drop Falling: 30-60 min
    • Water Molecule Interactions: fractions of a second
different scales different laws
Different Scales, Different Laws
  • Governing Equations different for different scales
  • Example: Modeling a Fluid:
    • River Flow: Navier-Stokes Equations
    • Interactions between fluid particles: Newton’s Molecular Dynamics
    • Atomic, Subatomic Description of Fluid Molecule: Schrödinger’s equations
model choice
Model Choice
  • Could represent river as discrete fluid particles, and utilize molecular dynamics to model its flow
  • More details included in the model, the more accurate your model will likely be
  • What’s the problem???

Good Luck trying to do this computationally!!!

computational difficulties
Computational Difficulties
  • Number of elements
  • Smaller Spatial Scale may warrant a smaller time scale in order to keep numerical methods stable
    • Example: CFL number for hyperbolic PDEs
model choice10
Model Choice
  • Balance detail and computational complexity
  • Choice often made to model a material as a continuum
  • Goal is to then find a constitutive law that can explain how the material behaves
  • If the material is homogeneous, the continuum assumption is typically acceptable and constitutive laws can be found
  • Heterogeneous materials are more difficult to model, and motivate the need for multiscale models
homogeneous elastic string
Homogeneous Elastic String
  • Discrete Scale: Mass-Spring system
  • point masses of mass
  • Springs between each mass have spring constant
  • In zero strain state, springs are length
  • Derive Equation for Longitudinal Motion
homogeneous elastic string12
Homogeneous Elastic String
  • Let be the displacement of mass from its zero strain state at time
  • Equation of motion for mass can be written using Newton’s Law:
  • The can be written as
homogeneous elastic string13
Homogeneous Elastic String
  • Forces felt by mass come from mass and mass
  • Net force on mass difference in forces from the left and right mass
homogeneous elastic string14
Homogeneous Elastic String
  • Full equation for mass
homogeneous elastic string15
Homogeneous Elastic String
  • Elasticity Modulus
  • Linear Mass Density
  • Take Limit as
homogeneous elastic string16
Homogeneous Elastic String
  • 1D Wave Equation
  • Continuum-Level model, limit of the microscopic (discrete) model
  • Wave speed determined by does NOT depend on location in the string
  • Hyperbolic PDE easy to simulate
inhomogeneous elastic string
Inhomogeneous Elastic String
  • Discrete Model, Mass-Spring system
  • Number of springs between each point mass can vary
inhomogeneous elastic string18
Inhomogeneous Elastic String
  • point masses of mass
  • Springs between each mass have spring constant
  • In zero strain state, springs are length
  • displacement of mass
  • = number of springs between mass and at time
inhomogeneous elastic string19
Inhomogeneous Elastic String
  • Equation of Motion for mass
inhomogeneous elastic string20
Inhomogeneous Elastic String
  • Equation of Motion for mass
  • Take Limit as
inhomogeneous elastic string21
Inhomogeneous Elastic String
  • Wave equation with locally varying wave speed:
  • To solve this wave equation you need to know but this is a microscopic quantity! (Local density of springs)
  • Micro quantity needed in a continuum equation
inhomogeneous elastic string22
Inhomogeneous Elastic String

Put another way in the form of a constitutive law (relation between stress and strain)

Dividing by h and taking the limit h  0

inhomogeneous elastic string23
Inhomogeneous Elastic String

Equating these two quantities gives:

Utilizing:

Elastic properties of the spring vary spatially

practical example
Practical Example
  • Rupturing String:
  • Assume springs break if the segment length for some distance
  • Microscopic Model:
  • Continuum Model:
practical example25
Practical Example
  • Begin with string in zero strain state attached at one end to wall
  • This string is stretched at the other end by a constant strain rate
  • 11 point masses, 100 springs between each pair of masses
  • When distance between masses exceeds then springs break
1d rupturing string27
1D Rupturing String

Ruptured Strings

Force

Displacement

Time Steps

challenges
Challenges
  • General problems with trying to couple a microscopic and continuum model
    • Number of elements in the microscopic scale
    • Carrying out the microscopic model for full continuum level time scale
    • If you only do a spatial or time sample of the microscopic evolution, how would you represent the micro-state at a later point in time?
overview of seminar topics
Overview of Seminar Topics
  • Interesting Medical/Biological Problems that would benefit from multiscale modeling
  • Models of the Cytoskeleton
  • Continuum Microscopic (CM) Methods
  • Probability Theory, PDF Estimation
  • CM modeling with statistical sampling
  • Solution Methods to Non-Linear Systems of Equations
  • And more…..
references
References
  • E W, Engquist B, “Multiscale Modeling and Computation”, Notices of the AMS, Vol 50:9, p. 1062-1070